Start here.
Start with a repeated handoff that has clear inputs, a clear outcome, and someone responsible when it goes wrong. Understand the exceptions before deciding how much of it should run automatically.
- Map the current process with the people doing it.
- Use ordinary rules when the decision is predictable.
- Give exceptions an owner and make failed work visible.
Look for repeated copying and waiting
Good candidates are often easy to describe: someone copies a form into another system, checks the same fields every day, prepares a recurring report, or sends a reminder after a known event. Ask where work sits waiting and where the same information gets entered twice.
Compare a few real examples, including one that went wrong. A flow that looks simple on a whiteboard may depend on staff recognizing an exception. That exception needs a place in the software.
Separate rules from interpretation
If an invoice is overdue by a known number of days, software can apply that rule without an AI model. If an incoming message needs to be understood or a long document needs a draft summary, AI may help with that particular step.
Keep the boundary clear. A suggested category or draft reply can go to a person for review, while routine record updates follow explicit rules. Adding AI to every step makes the process harder to explain and diagnose.
Route a request without making a promise
In an illustrative intake queue, software can attach the original request, suggest a category, identify the responsible reviewer, and flag missing information. The accepted handoff is a usable packet for that person, not an automated commitment about what the business will do.
Define routing categories from real examples, including requests that fit none of them. Keep the source and reason beside a suggestion so the reviewer can correct it. When the question needs judgment or an unavailable fact, retain it in an explicit review state rather than treating the suggested category as a finished response.
Make the handoff dependable
Connecting two tools involves more than moving the happy-path data. Decide which system owns each record, how duplicates are detected, and what happens if the receiving tool is temporarily unavailable.
Our own products rely on connected workflows across catalog, finance, and collaboration. That experience informs the questions we ask about a custom integration; it is not a promise that every third-party system will offer the access you need.
- Record whether the action succeeded or failed.
- Retry safely without creating duplicate work.
- Put unresolved items where someone will see them.
- Keep enough context for a person to finish the task.
Try a small slice and compare it with today
Run the first version on a limited set of work. Compare time spent, manual edits, missed items, and the experience of the people using it. A process that runs faster but creates a new daily cleanup task may not be an improvement.
Choose the next step from what you observe. You might expand the integration, simplify the workflow, or leave a judgment-heavy step with a person. You should be able to explain why the change is useful before widening the scope.
Map one real example
Follow a recent piece of work through the whole process. Four questions usually expose the useful starting point.
- Input
- What starts the work, and where does the information arrive?
- Decision
- Which parts follow a rule, and which require judgment?
- Handoff
- Who or what needs the result, and in what format?
- Exception
- What happens when information is missing or a system is unavailable?
A couple of common questions.
Does automation always need AI?
No. Many useful automations are ordinary integrations and rules. AI is useful when a step involves interpreting language or unstructured information.
Can you work with the tools we already use?
We start there. Feasibility depends on the access those tools provide, the quality of their data, and the workflow you want to improve.